The present invention relates to methods for video data coding, and more particularly to a method for data coding, which is favorable for video communication at low bit rate, outline picture image forming for storage and retrieval of picture image file, and further for coding system to code video data or voice data with abnormal value generation efficiently according to probability distribution.
Methods for data coding in the prior art are roughly classified into predictive coding method and transform coding method.
In the predictive coding method, brightness values of a picture element as unit is predicted from an adjacent picture element, and the predicted value is subjected to variable length coding or quantized.
On the other hand, in the transform coding method, picture image is divided into blocks each having definite size (8.times.8, 16.times.16 or the like being frequently used) and the block as unit is subjected to orthogonal transform and coded.
In this method, since global redundancy beyond the block size represented by the flat background or the like cannot be utilized, its compression ratio is limited.
Further, various methods such as vector quantizing method or block coding method are known as disclosed in Hosaka et.: "Comparison of Still Picture Coding Methods" (Electronic Communication Society Technical Paper IE83-106). Among these, as a method having particularly high compression performance, adaptive cosine transform encoding method being a sort of orthogonal transform encoding method is known. This method has problems in that not only the amount to be processed increases but also sharp line or edge (e.g., character or symbol on image of light and shade) fades in the decoded image. Also in vector quantizing method studied actively in recent years, since the image is divided into sub-blocks of definite size and quantizing is effected per block, similar limitation exists.
Further, in quantizing method disclosed in J.MAX: "Quantizing for Minimum Distortion" IRE Tr. on Information Theory (March, 1960), probability distribution is assumed regarding input signals, and quantizing level is determined so that quantizing distortion (quadratic error) becomes minimum with respect to the distribution. Gaussian distribution is frequently used as the probability distribution because such condition frequently occurs where the central limiting theorem (theorem that sum of N independent random variables in arbitrary distribution approaches the Gaussian distribution as N becomes large) can be applied. This method has problems in that when the distribution of input signals is shifted from the assumed probability distribution, for example, assuming the Gaussian distribution, when generation probability of abnormal value beyond 3.sigma.(.sigma.: standard deviation) increases to 1--several % from 0.3% being theoretical value, mean quantizing error increases abruptly.
The prior art as above described has problems in that, in the case of method with high compression performance such as orthogonal transform encoding or vector quantizing, the encoding/decoding processing amount increases and therefore the hardware cost increases, or time is required for decoding. In the case of method with simple processing such as prediction encoding or block encoding, the compression performance is deteriorated.
The above-mentioned adaptive cosine transform encoding method is disclosed in W. Chen, et al: "Adaptive Coding of Monochrome and Color Images" [IE.sup.3 Vol. COM-25, 11(1977)]. In this method, since selective preservation of useful image information is not considered, if it is intended to increase the compression ratio, the useful image information will be emitted.
Finally, enciphering method suitable for the high efficiency encoding in the prior art will be described. Among the small number of articles already published, method disclosed in "Cipher Encoding System Utilizing Feature of Graphic Information" (Electronic Communication Society Technical Paper IE82-98) repeatedly applies the quadratic differentiation operation directly to the original image data. As a result, the obtained enciphering data becomes pattern with little redundancy in similar manner to noise. Consequently, this method is not suitable for the high efficiency encoding, and cost for the storage and transmission of the image data increases.